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    pdftitle={Predicting indoor topology labelings and structure from a large indoor topological database},    % title
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\title{\LARGE \bf
Predicting indoor topology labelings and structure\\ from a large indoor topological database 
}

%\author{ \parbox{3 in}{\centering Huibert Kwakernaak*
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%         Faculty of Electrical Engineering, Mathematics and Computer Science\\
%         University of Twente\\
%         7500 AE Enschede, The Netherlands\\
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%         \parbox{3 in}{ \centering Pradeep Misra**
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%        Department of Electrical Engineering \\
%         Wright State University\\
%         Dayton, OH 45435, USA\\
%         {\tt\small pmisra@cs.wright.edu}}
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\title{\LARGE \bf
Predicting indoor topology labelings and structure\\ from a large indoor topological database }

\author{A. Aydemir, E. J\"arleberg, P. Jensfelt % <-this % stops a space
\thanks{The authors are with the Centre for Autonomous Systems at the Royal Institute of Technology (KTH), Stockholm, SE-100 44, Sweden.    {\tt\footnotesize [aydemir,erikjar,patric]@csc.kth.se}}%
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\begin{abstract}
Topological maps are suitable in a multitude of robotic applications for their various properties. Recently there has been an increased interest in augmenting topological maps by inferring semantic labels from sensory data. Such a model can be used to predict what lies ahead in the topology for various robotic tasks.
In this work we analyze a large indoor floorplan database consisting of 567 floors, 6426 spaces with 91 room types and 8446 connections between rooms corresponding to real places. We present a system that, given a partial graph, predicts the rest of the topology by building a model from a large real world annotated floorplan database. Our hypothesis is that indoor topologies consists of multiple smaller functional parts. We demonstrate the applicability of our approach with experimental results.

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\section{INTRODUCTION}
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\section{SYSTEM OVERVIEW} 
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\section{The Method}
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\section{Experiments}
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\section{Conclusion and Future Work}
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\section{Acknowledgements}
The authors thank Emily Whiting, Seth Teller and the RVSN group at MIT CSAIL\footnote[1]{\url{http://rvsn.csail.mit.edu}} for their helpful pointer to acquire the dataset. We are also grateful to John Folkesson and Kristoffer Sj\"o\"o for their feedback on an early draft of the paper.
This work was supported by the EU FP7 project CogX.
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